code: def hypergeometric(
      random_state,
      ngood,
      nbad,
      nsample,
      size=None,
      chunk_size=None,
      gpu=None,
      dtype=None,
  ):
      r"""
      Draw samples from a Hypergeometric distribution.
  
      Samples are drawn from a hypergeometric distribution with specified
      parameters, ngood (ways to make a good selection), nbad (ways to make
      a bad selection), and nsample = number of items sampled, which is less
      than or equal to the sum ngood + nbad.
  
      Parameters
      ----------
      ngood : int or array_like of ints
          Number of ways to make a good selection.  Must be nonnegative.
      nbad : int or array_like of ints
          Number of ways to make a bad selection.  Must be nonnegative.
      nsample : int or array_like of ints
          Number of items sampled.  Must be at least 1 and at most
          ``ngood + nbad``.
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  If size is ``None`` (default),
          a single value is returned if ``ngood``, ``nbad``, and ``nsample``
          are all scalars.  Otherwise, ``np.broadcast(ngood, nbad, nsample).size``
          samples are drawn.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      out : Tensor or scalar
          Drawn samples from the parameterized hypergeometric distribution.
  
      See Also
      --------
      scipy.stats.hypergeom : probability density function, distribution or
          cumulative density function, etc.
  
      Notes
      -----
      The probability density for the Hypergeometric distribution is
  
      .. math:: P(x) = \frac{\binom{m}{n}\binom{N-m}{n-x}}{\binom{N}{n}},
  
      where :math:`0 \le x \le m` and :math:`n+m-N \le x \le n`
  
      for P(x) the probability of x successes, n = ngood, m = nbad, and
      N = number of samples.
  
      Consider an urn with black and white marbles in it, ngood of them
      black and nbad are white. If you draw nsample balls without
      replacement, then the hypergeometric distribution describes the
      distribution of black balls in the drawn sample.
  
      Note that this distribution is very similar to the binomial
      distribution, except that in this case, samples are drawn without
      replacement, whereas in the Binomial case samples are drawn with
      replacement (or the sample space is infinite). As the sample space
      becomes large, this distribution approaches the binomial.
  
      References
      ----------
      .. [1] Lentner, Marvin, "Elementary Applied Statistics", Bogden
             and Quigley, 1972.
      .. [2] Weisstein, Eric W. "Hypergeometric Distribution." From
             MathWorld--A Wolfram Web Resource.
             http://mathworld.wolfram.com/HypergeometricDistribution.html
      .. [3] Wikipedia, "Hypergeometric distribution",
             http://en.wikipedia.org/wiki/Hypergeometric_distribution
  
      Examples
      --------
      Draw samples from the distribution:
  
      >>> import mars.tensor as mt
  
      >>> ngood, nbad, nsamp = 100, 2, 10
      # number of good, number of bad, and number of samples
      >>> s = mt.random.hypergeometric(ngood, nbad, nsamp, 1000)
      >>> hist(s)
      #   note that it is very unlikely to grab both bad items
  
      Suppose you have an urn with 15 white and 15 black marbles.
      If you pull 15 marbles at random, how likely is it that
      12 or more of them are one color?
  
      >>> s = mt.random.hypergeometric(15, 15, 15, 100000)
      >>> (mt.sum(s>=12)/100000. + mt.sum(s<=3)/100000.).execute()
      #   answer = 0.003 ... pretty unlikely!
      """
      if dtype is None:
          dtype = (
              np.random.RandomState()
              .hypergeometric(
                  handle_array(ngood),
                  handle_array(nbad),
                  handle_array(nsample),
                  size=(0,),
              )
              .dtype
          )
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorHypergeometric(
          seed=seed, size=size, gpu=gpu, dtype=dtype
      )
      return op(ngood, nbad, nsample, chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/hypergeometric.py
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import AnyField
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, handle_array, TensorDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: hypergeometric(random_state, ngood, nbad, nsample, size, chunk_size, gpu, dtype) and returns: op